DEV Community: Xiao Rui

YourGPT Just Open-Sourced a Copilot SDK for Building Context-Aware AI Inside SaaS Products

Xiao Rui — Wed, 04 Feb 2026 11:11:05 +0000

AI chatbots have become a standard part of modern SaaS products.

They answer support questions, guide users through features, and help teams scale user assistance. In many applications today, it’s normal to see an AI assistant available the moment you log in.

That first phase of in-product AI focused mainly on conversation.

For everyday support and guidance, it works well.

As more teams begin using AI across reporting, operations, user management, and internal workflows, expectations are naturally expanding. People now want AI that not only responds, but understands what’s happening inside the product and helps move work forward.

That evolution from conversational assistants to in-product copilots is where things start becoming truly powerful.

This week, YourGPT released an open-source Copilot SDK designed specifically to bring real product context into AI-driven experiences.

Instead of sitting beside the product, the AI becomes part of it.

When AI Understands the Workflow

Here’s a familiar situation.

They’re working inside an admin panel, reviewing a filtered list of customer records while preparing a quarterly report. A bulk field update is needed, followed by exporting the data.
The copilot is already present inside the workflow.

It understands:
• the current page in the application
• which records are selected
• the active filters
• user permissions
• the task in progress

There’s no need to explain context.
The system already knows the environment.
This is where YourGPT Copilot SDK feels fundamentally different.

Instead of starting with a generic prompt, the copilot operates with real-time product awareness and moves directly into action. Tasks that once required multiple steps across dashboards happen within a single intelligent flow.

From Conversation to Participation

Most in-product AI today focuses on answering questions.

That’s incredibly useful for guidance, documentation, and onboarding.
Copilots take the next step.
They don’t just talk about work.
They participate in it.

Instead of existing only as a chat interface, they understand:

• Current product state
• Live data and selections
• User roles and permissions
• Available actions and workflows

This allows AI to support real tasks directly inside the product experience.

What the Copilot SDK Enables

YourGPT’s Copilot SDK is built around this context-first approach.

The copilot can access:

• Real-time app state
• User-specific permissions
• Active datasets and views
• Backend actions and workflows
• Persistent session memory
So when a user asks something, the AI already understands the situation.
Not just the message.

The full product context.

A practical example

You’re viewing 247 customers filtered by signup date and subscription tier.

Instead of walking through menus, a copilot can recognize the context and respond:

“Want me to export these 247 customers as a CSV formatted for your Q1 report?”

And then complete the task instantly.
No step-by-step instructions.
No re-explaining the workflow.
Just execution.
That’s where AI starts saving meaningful time.

From Helpful Responses to Real Actions

With copilots powered by the SDK, AI can:

• Call backend APIs
• Update records
• Trigger workflows
• Generate reports and files
• Render UI components like tables and forms

AI becomes an operational layer inside the product.
You’re no longer just asking for guidance.
You’re delegating work.

Why Context-Aware AI Matters for SaaS

AI is now expected in software.

But the real impact comes from how deeply it’s integrated into workflows.
Conversation helps users learn.
Context-aware copilots help users complete work faster, with fewer clicks and less friction.

For internal tools, dashboards, admin panels, and enterprise platforms, this leads to:

• Faster task completion
• Reduced manual effort
• Cleaner user experience
• Better productivity

People don’t want more interfaces.
They want software that understands their work.

The Open-Source Advantage

Making the Copilot SDK open source removes a major barrier to building this kind of AI.

Teams can:

• Choose any LLM provider
• Control data and infrastructure
• Customize workflows deeply
• Avoid vendor lock-in

It also eliminates months of engineering effort required to build:

• Context pipelines
• Action execution systems
• Memory handling
• UI integrations

Developers can focus on product logic instead of AI plumbing.

A Signal of Where In-Product AI Is Headed

We’re moving beyond AI as a feature.

Toward AI as product infrastructure.
Instead of assistants that wait for instructions, copilots operate within the product itself, aware of state, data, and workflows.

That’s what turns AI from helpful into truly transformative for everyday work.

As more products adopt this approach, expectations will shift quickly.
Context-aware AI will become the new standard.

7 Best Alternatives to Botpress in 2025

Xiao Rui — Thu, 13 Nov 2025 12:05:18 +0000

AI chatbots have become essential for businesses looking to improve customer support, automate workflows, and streamline operations. While Botpress is still popular among developers for its open-source flexibility, many teams now prefer tools that offer faster setup, stronger AI capabilities, and easier deployment across multiple channels.
More companies are moving toward platforms that reduce technical complexity and provide AI agents that work immediately. Industry data shows that organizations adopting modern conversational AI see higher productivity, reduced support load, and better customer satisfaction.
Below are the 7 best Botpress alternatives for 2025 with clear descriptions, use cases, and limitations. This will help you choose a platform that fits your skills, automation needs, and growth plans.

1. YourGPT

YourGPT is a complete AI platform for support, sales, and workflow automation. It allows teams to build and deploy AI agents without writing code. You can train the system using PDFs, websites, Notion pages, Google Sheets, internal knowledge bases, and CRM data. Deployment is available across web chat, WhatsApp, Instagram, Slack, Telegram, email, and voice, making it suitable for both customer-facing and internal workflows.
YourGPT continuously improves through real interactions and adapts to your brand tone, helping teams deliver fast, accurate, and consistent responses at scale**.

AI Capabilities

No-code AI agent builder for quick setup
Custom knowledge bases trained on your business data
Omnichannel deployment across web, social platforms, and voice
AI agents that learn and improve through real conversations
Live agent escalation through CRM, email, Slack, or dashboard
Workflow studio for automating internal tasks and operations
Support for advanced logic, APIs, and conditional flows
Built-in analytics to track performance and response quality

Limitations

Certain advanced features available only in paid plans

Regular feature updates may require brief adaptation

Best For

Teams looking for practical AI automation without coding. Suitable for support, sales, and internal operations across small businesses, startups, and enterprise teams.

2. Voiceflow

Voiceflow is built for teams designing structured chat or voice assistants together. The visual builder brings designers, developers, and product teams into one workspace.
It supports major models like GPT-4 and Claude, making it suitable for prototyping and deploying assistants across web, mobile apps, and telephony.

AI Capabilities

Visual drag-and-drop builder
Real-time collaboration for teams
Support for multiple LLMs including GPT-4 and Claude
Unified design for chat and voice flows
Instant prototyping and testing tools

Limitations

Some integrations require technical support

No native live chat features

Best For

Teams focused on designing structured conversational experiences.

3. Intercom

Intercom stands out as a full customer engagement platform that blends AI, automation, and human support. It goes beyond chatbot creation by offering tools for onboarding, messaging, self-service, and lifecycle communication, making it a strong alternative for SaaS companies looking for an all-in-one communication layer.
Intercom is widely used by SaaS companies for customer messaging, onboarding, and automation. Its AI chatbot handles routine questions while human agents manage complex issues from a unified inbox.

AI Capabilities

AI chatbots for FAQs and routing
Unified inbox across email, social, and web
Product tours and onboarding flows
Customer data platform for personalization
Integrations with Salesforce, HubSpot, and Slack

Limitations

Costs increase with higher message volume

Some workflows require technical setup

Best For

SaaS teams that need multi-channel customer messaging and onboarding.

4. Yellow.ai

Yellow.ai is built for large organizations that require enterprise-grade automation across global markets. It excels in multilingual capabilities, high-volume workflows, and deep system integrations, making it a strong Botpress alternative for operational teams working at scale.
Yellow.ai helps global companies manage multilingual and large-scale customer interactions. It supports chat and voice automation across departments and integrates with ERP, CRM, HRMS, and internal systems.

AI Capabilities

Advanced NLP for web, mobile, social, and voice bots
Support for 135+ languages
Automation across customer service and internal workflows
Enterprise integrations for CRM, ERP, and HRMS
Real-time analytics and monitoring

Limitations

Higher cost for smaller teams

Requires technical expertise for full implementation

Best For

Large organizations that need multilingual and enterprise-level automation.

5. Rasa

Rasa is a strong fit for engineering-focused companies that need complete control over how their chatbot works. It offers deep customization, full access to model pipelines, and enterprise-level security, making it a preferred alternative for teams that want to build AI from the ground up.
Rasa is an open-source AI framework that gives developers full control over models, data, and deployment. It is known for its customization options and on-premise hosting.

AI Capabilities

Full code-level customization
On-premise deployment for data control
Modular NLU and dialogue pipelines
Extensive SDKs for engineers and ML teams
Suitable for regulated industries

Limitations

Requires strong programming experience

Not suitable for no-code or low-code users

Best For

Companies needing deep customization and strict compliance.

6. Landbot

Landbot is ideal for marketers and small teams that want visually appealing chatbots without technical barriers. It offers conversational landing pages and interactive flows that help improve lead capture and engagement, making it a lighter alternative to more complex platforms like Botpress.
Landbot focuses on visually interactive chatbots and conversational landing pages. It is simple to use and ideal for marketers and small teams that need quick deployment without technical work.

AI Capabilities

Visual drag-and-drop builder
Conversational landing pages for lead capture
Web, WhatsApp, and Messenger deployment
Ready-to-use templates for campaigns
CRM and email marketing integrations

Limitations

Limited advanced AI features

Not built for complex enterprise workflows

Best For

Marketing teams building conversational funnels and campaign bots.

7. Kore.ai

Kore.ai is designed for enterprises that require scalable automation with strict compliance and security standards. It offers industry-specific solutions for banking, healthcare, retail, and telecom, giving it a clear advantage in regulated environments.
Kore.ai offers no-code and low-code tools with advanced enterprise AI. It includes industry-focused models for banking, healthcare, and telecom.

AI Capabilities

Hybrid no-code and low-code builder
Pretrained models for specific industries
Voice IVR and omnichannel support
Automated task workflows with human escalation
Compliance and detailed analytics

Limitations

Learning curve for smaller teams

Pricing aligns with enterprise budgets

Best For

Large enterprises with regulated workflows and high automation needs.

How to Choose the Right Botpress Alternative

Use this simple checklist to make a smart decision:

Set clear goals Know if you want to automate support, sales, internal tasks, or all three.
Identify real bottlenecks Pick tools that remove repetitive work such as FAQs, routing, booking, or data lookup.
Evaluate ease of use vs. customization Some platforms focus on speed (YourGPT, Landbot) while others provide full control (Rasa).
Check integrations Make sure the platform connects to your CRM, ticketing tool, or ecommerce system.
Review cost vs. value Focus on ROI and long-term workload reduction.
Test with real scenarios Use trial versions and measure accuracy, speed, and team adoption.
Confirm data protection Choose tools with strong security and compliance features.

Conclusion

AI chatbots have become essential for modern businesses aiming to streamline support, sales, and internal workflows. While Botpress delivers strong capabilities for technical teams, many organizations now look for platforms that combine faster setup, stronger AI accuracy, reliable integrations, and smooth scaling as they grow.
YourGPT provides a complete no-code solution for teams that want practical automation without technical complexity. Voiceflow and Landbot work well for design-driven use cases. Rasa, Yellow.ai, and Kore.ai are better suited for advanced customization and enterprise environments. By choosing a platform that fits your goals, skill level, and workflow needs, your business can automate repetitive tasks, reduce response time, and deliver consistent customer experiences throughout 2025 and beyond.

Why Businesses Are Turning to Custom GPT Chatbots in 2025

Xiao Rui — Wed, 24 Sep 2025 10:34:29 +0000

Think about the last time you used a chatbot. Maybe you wanted to check an order status or ask about returns. You typed your question, and the bot replied with the same copy-paste line: “Please check our FAQ.” You tried again, got the same answer, and gave up.

That was the old world of chatbots. In 2025, businesses are switching to something better: custom GPT chatbots. These bots aren’t limited by scripts. They’re trained on company knowledge like FAQs, manuals, and policies, which means they can provide context-aware answers that actually help customers.

The shift is happening fast because customer expectations are different now. People don’t wait. If you can’t answer their questions in real time, they’ll move on to someone who can.

What Is a Custom GPT Chatbot

A custom GPT chatbot is an AI-powered assistant built around your business knowledge. It differs from older bots in four major ways:

Data-driven training
It learns from your documents, articles, and past support tickets instead of rigid pre-set scripts.
Contextual understanding
It interprets intent. If a customer says “I can’t log in,” the chatbot troubleshoots access issues instead of just keyword-matching “login.”
Tone and personality
Businesses can shape the bot’s style, professional for finance, casual for retail, empathetic for healthcare.
Omnichannel use
Once trained, it can run on your website, WhatsApp, Messenger, Slack, or even HR portals.

Think of it as a digital team member that never sleeps, never gets tired, and never loses patience.

Why They Matter in 2025

Customer behavior has shifted dramatically. According to HubSpot 90% of customers expect an immediate response when they reach out online. Email replies within 24 hours used to be fine. Now, even 10 minutes can feel too long.

For businesses, this creates three challenges:

Speed: Customers demand near-instant replies.
Cost: Scaling support teams is expensive. Salaries, training, and turnover add up quickly.
Consistency: Different agents often give different answers. A chatbot guarantees accuracy every time.

And then there’s scalability. During high-demand events like Black Friday or tax season, a chatbot can handle thousands of conversations at once. No human team can match that.

For many businesses, the goal isn’t replacing people. It’s building a hybrid model where chatbots handle repetitive queries while humans focus on high-value, complex conversations.

How to Build One That Works

Too many companies rush chatbot launches and end up with bots that frustrate customers. Success comes from treating it as an evolving project.

Start with a clear role
Choose one use case, order tracking, onboarding, or policy questions. Get one thing right before expanding.
Gather and clean training data
Collect FAQs, guides, policies, and transcripts. Remove outdated info and keep wording simple. Good data equals good answers.
Pick a no-code platform
Choose a tool that allows bulk uploads, custom branding, and multi-channel deployment. Analytics dashboards are a plus.
Test with messy questions
Customers don’t type neatly. Use slang, typos, and half-finished sentences when testing to see how the bot performs.
Launch in stages
Start on one channel, collect feedback, then expand to others.
Keep it updated
Review and refresh monthly. A chatbot giving outdated policy info is worse than no chatbot at all.

Where They’re Already Making a Difference

Businesses across industries are proving how valuable GPT chatbots can be:

E-commerce
An online fashion brand shared that its chatbot now handles most “Where is my order?” queries without human involvement. Support teams can finally focus on returns and complex customer requests.
SaaS
A project management startup noticed that more users completed onboarding after deploying a chatbot to guide new customers in real time. Instead of reading long documentation, users got instant help.
Healthcare
Clinics use GPT chatbots for appointment scheduling, prescription refills, and reminders. They also provide after-hours support, giving patients more flexibility and control over their care.
Education
Universities often drown in admissions queries. Chatbots handle repetitive questions about deadlines, fees, and eligibility, reducing admin workload and improving response times for students.
HR and Internal Support
Employees can ask chatbots about leave policies or payroll issues inside Slack or Teams. This saves time and makes support feel more accessible.

Wherever repetitive questions pile up, chatbots are making a real difference.

Mistakes to Avoid

Even advanced bots fail if set up poorly. Common pitfalls include:

Launching without a defined purpose.
Forgetting to update knowledge bases.
Blocking human handoff when customers need it.
Automating everything and ignoring empathy.

The best approach blends automation with human backup. A good chatbot knows when to step aside.

What’s Coming Next

Today’s GPT chatbots are great at providing answers. The next big leap is letting them take action.

Refunds and returns: Bots can automatically initiate returns once eligibility is confirmed.
Purchases inside chat: Customers could ask about a product and buy it without leaving the conversation.
Account updates: Changing billing info or addresses could happen instantly with AI assistance.
Personalized recommendations: With purchase history access, bots can suggest products or plans tailored to each customer.

According to Gartner, by 2027 80% of customer interactions will be handled by AI-driven automation. Businesses that prepare now will already have the trust, infrastructure, and data when that future arrives.

Final Thought

A custom GPT chatbot is not about cutting costs at all costs. It’s about giving customers what they already expect: fast, accurate, and consistent service.

The difference between success and failure lies in execution. A chatbot that is ignored after launch will frustrate people. One that’s updated regularly, trained with real company data, and given a clear role becomes a long-term asset.

Done right, it doesn’t feel robotic. It feels like an extra teammate who takes care of the repetitive work, giving your human staff space to focus on empathy and problem-solving.

Platforms like YourGPT make it possible to build and train these chatbots without writing a single line of code. You can upload your own data, customize the tone, and deploy across channels in weeks.

In 2025, this isn’t about being ahead anymore. It’s about keeping up. The companies that adopt now are setting the new baseline.

10 Common Vulnerabilities in Large Language Models (LLMs)

Xiao Rui — Wed, 29 Jan 2025 07:39:53 +0000

Large Language Models (LLMs) are advanced neural network-based AI systems that mostly use transformer architectures to process and generate text that appears to be like human-written. They are trained on extensive text corpora, they uses deep learning techniques to understand and predict linguistic patterns through probabilistic modeling.

These models leverage complex mathematical representations called embeddings to capture semantic relationships between words. By analysing vast training datasets, LLMs have developed an advanced language understanding capabilities that enable them to perform tasks like completition, translation, summarisation, and contextual text generation.

With such impressive capabilities, LLMs have become widely used in various application in our day to day life. But they come with their own set of limitations. Below, we have listed 11 common vulnerabilities in Large Language Models and their implications.

#10 Common Vulnerabilities in Large Language Models: Overview

Understand the vulnerabilities of large language models (LLMs) and the risks they pose to AI security:

Prompt Injections: Prompt injections are a big security risk where users can manipulate an AI’s actions by feeding it certain inputs, even if those inputs are not visible or obvious to humans. These hidden manipulations can make the AI behave in ways it wasn’t intended to.

An attacker could create a prompt that forces the AI to ignore its original instructions, which could lead to revealing sensitive information or making the AI act against its rules. This issue highlights the difficulty in ensuring AI systems follow their intended guidelines and remain secure.
Prompt Leaking: Prompt leaking happens when the AI’s internal instructions or prompts are accidentally exposed. These instructions could contain sensitive information that could be used by bad actors.

If these instructions reveal things like access credentials, an attacker could figure out how the AI works or how it’s set up. This kind of leak can lead to unauthorized access or manipulation of the system, creating serious security issues.
Model Stealing: Model stealing is when someone tries to copy or steal a language model, either in whole or in part. The attacker usually does this by recording a lot of interactions with the target model, then using that data to train a new model that behaves like the original.

This kind of attack can be dangerous, as it may be used to steal intellectual property or break licensing rules, leading to serious security and legal problems.
Data and Model Poisoning: Data and model poisoning is when someone intentionally changes the data used to train an AI, aiming to create weaknesses, biases, or hidden issues in the system. This can seriously affect how secure, effective, and ethical the AI is.

Attackers might add specific data to influence the AI’s responses, set up hidden triggers, or introduce biases that can be taken advantage of later. Because the changes are often subtle, it can be hard to notice or prevent them.
Sensitive Information Disclosure: Sensitive information disclosure happens when an AI accidentally reveals private or confidential data. This can lead to serious privacy issues and security concerns.

For example, the AI might unintentionally share personal details, financial info, or business secrets during conversations. This is especially risky for systems that handle sensitive customer or company data.
Vector and Embedding Weaknesses: In systems using Retrieval Augmented Generation (RAG), vector and embedding methods can create security issues that traditional security checks might miss.

Attacks like embedding poisoning can retrieve harmful data, while manipulating the vector structure can bypass security filters. Additionally, flaws in how embeddings are handled can expose sensitive information.
Unbounded Consumption: Unbounded consumption happens when an LLM application, like ChatGPT Pro, allows excessive use of computational resources. This can lead to problems like service outages, financial losses, model theft, and drained resources.

Attackers can take advantage of this by sending a large number of complex requests, overwhelming the system, causing disruptions, and increasing costs.
Library Injection Exploit: Trojanized This is known as "supply chain vulnerabilities," where attackers create fake versions of libraries or LLMs and disguise them as trusted services. Users, unaware of the malicious code, might download and use these models, thinking they're legitimate.

After being integrated, attackers can control the model to access sensitive information or carry out unauthorized actions.
Zero-Day Vulnerabilities: Zero Day Flags are serious security flaws in AI systems that attackers often find before anyone else, including the security teams. Since there’s no quick fix, these vulnerabilities can be taken advantage of until a solution is found and implemented.

This gives attackers a chance to exploit the weakness, potentially causing damage until a patch is released.
Misinformation Generation: Misinformation is a big problem with LLMs, as they can produce content that seems true but is actually false or misleading. This can cause users to make decisions based on wrong information, leading to serious consequences.

AI is already being used to create fake stories, false statistics, and made-up explanations that sound believable. Some people are using fake accounts and AI to spread false content and influence public opinion, which is impacting important decisions and could lead to serious effects.

LLMs are powerful, but their vulnerabilities show why strong security measures are essential. Regular audits, strict input/output validation, and careful training data management can help reduce these risks and create safer AI systems. Check out these awesome resources linked below for more details:

https://owasp.org/www-project-top-10-for-large-language-model-applications/

https://yourgpt.ai/blog/growth/how-to-hack-large-language-models-llm

If you have some more good resources drop them on comments below.